Objectives of Programme's Learning Outcomes

• Have integrated and elaborate mathematical knowledge.
• Read research articles in at least one discipline of mathematics.
• Carry out major projects.
• Appropriately use bibliographic resources for the intended purpose.
• Present the objectives and results of a project orally and in writing.
• Apply innovative methods to solve an unprecedented problem in mathematics or within its applications.
• Appropriately make use of computer tools, as required by developing a small programme.
• Communicate clearly.
• Communicate the results of mathematical or related fields, both orally and in writing, by adapting to the public.
• make a structured and reasoned presentation of the content and principles underlying a piece of work, mobilised skills and the conclusions it leads to.
• Have sufficient knowledge of English for basic scientific communication.
• Adapt to different contexts.
• Demonstrate thoroughness, independence, creativity, intellectual honesty, and ethical values.

Learning Outcomes of UE

Knowledge Representation & Reasoning (KR&R) is a branch of Artificial Intelligence which uses logic languages for (a) representing information and knowledge, and (b) automatic reasoning on top of these representations. In this course, students will get acquainted with some recent technologies developed in the domain of KR&R, and will develop competencies that enable them to represent and solve computational problems by using the most appropriate logic formalism for the problem at hand. 

Content of UE

This course will focus on the following two applications of KR&R in particular:
(1) KR&R as the engine driving the Semantic Web, which is based on the formalism of Description Logic (DL) and implemented in the  W3C Web Ontology Language (OWL).
(2) KR&R for representing and solving problems in the complexity class NP (including all NP-complete problems). This application is based on the formalism known as Answer Set Programming (ASP).

Prior Experience

Students should be familiar with the foundations of propostional and first-order logic (which are taught, for example, in the courses Bases de Données I and II).

Type of Assessment for UE in Q2

• Presentation and/or works
• Written examination

Q2 UE Assessment Comments

The weight of the personal assignment can vary between 10% and 40% of the final mark; the weight that is most favorable to the student will be applied.

Type of Assessment for UE in Q3

• Presentation and/or works
• Written examination

Q3 UE Assessment Comments

The weight of the personal assignment can vary between 10% and 40% of the final mark; the weight that is most favorable to the student will be applied.